data and analytics

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Published By: IBM     Published Date: Oct 06, 2015
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
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networks, ibm, redefining networks, cloud, analytics, mobile, social, security, big data, it security, data center, enterprise networks, it management, data management
    
IBM
Published By: IBM     Published Date: Nov 09, 2015
IBM believes the Data Warehouse market continues to expand and adapt to address new requirements for user self-service, increased agility, requirements for new data types, lower cost solutions, adoption of open source, driving better business insight, and faster time to value.
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ibm, data, magic quadrant, data management, analytics, business technology
    
IBM
Published By: IBM     Published Date: Nov 16, 2015
As vendors continue to evolve their solutions to fit these changing requirements, IBM remains a leader in this Gartner Magic Quadrant.
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ibm, data warehouse, data management, analytics, gartner, business technology, data center
    
IBM
Published By: IBM     Published Date: Jan 13, 2016
By using InfoSphere Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of-impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time.
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ibm, data, infosphere, server, data warehousing, enterprise applications, data management, business technology
    
IBM
Published By: IBM     Published Date: Feb 29, 2016
This paper explores the implications of cloud, big data and analytics, mobile, social business and the evolving IT security landscape on data center and enterprise networks and the changes that organizations will need to make in order to capitalize on these technology force.
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ibm, network, cloud, analytics, mobile, social, big data, it security, networking, security, enterprise applications, business technology
    
IBM
Published By: IBM     Published Date: Apr 18, 2016
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
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ibm, mdm, big data, data management, data matching, customer analytics
    
IBM
Published By: IBM     Published Date: Apr 18, 2016
By using InfoSphere Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of-impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time.
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ibm, big data, ibm infosphere, information empowerment, data integration, data management
    
IBM
Published By: IBM     Published Date: Jul 12, 2016
Join us for a complimentary webinar with Mark Simmonds, IBM big data IT Architect who will talk with leading analyst Mike Ferguson of Intelligent Business Strategies about the current fraud landscape. They will discuss the business impact of fraud, how to develop a fraud-protection strategy and how IBM z Systems analytics solutions and predictive models can dramatically reduce your risk exposure and loss from fraud.
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ibm, z systems, fraud loss reduction, fraud management, fraud prevention, fraud analytics, roi, security
    
IBM
Published By: IBM     Published Date: Oct 18, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, enterprise applications, data management, data center
    
IBM
Published By: IBM     Published Date: Apr 14, 2017
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
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customer analytics, data analysis, competitive advantage, understanding your customer base
    
IBM
Published By: Group M_IBM Q418     Published Date: Sep 10, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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Group M_IBM Q418
Published By: Cisco     Published Date: Jul 11, 2016
Companies rely on an expanding set of applications to compete in today's rapidly evolving business environment: - They rely on a fast-growing array of applications and devices (email, collaboration tools, and smartphones/tablets) to communicate and conduct business with customers and business partners. - They are creating, collecting, and repurposing large, unstructured data sets in life sciences, geophysics, media, and manufacturing. - They are collecting, storing, and analyzing more social and sensor-generated data about environments, products, customers, and transactions. The promise of better and faster data-driven decision making based on all this information is pushing big data and analytics (BDA) technology to the top of executive agendas. To succeed, CIOs must place a laserlike investment focus on datacenter solutions that allow them to deliver scalable, reliable, and flexible infrastructure for fast-growing BDA environments. Read more to learn how!
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Cisco
Published By: Cisco     Published Date: Jul 11, 2016
CTOs, CIOs, and application architects need access to datacenter facilities capable of handling the broad range of content serving, Big Data/analytics, and archiving functions associated with the systems of engagement and insight that they depend upon to better service customers and enhance business outcomes. They need to enhance their existing datacenters, they need to accelerate the building of new datacenters in new geographies, and they need to take greater advantage of advanced, sophisticated datacenters designed, built, and operated by service providers. IDC terms this business and datacenter transformation the shift to the 3rd Platform.
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Cisco
Published By: Cisco     Published Date: Jul 11, 2016
Today's datacenter networks must better adapt to and accommodate business-critical application workloads. Datacenters will have to increasingly adapt to virtualized workloads and to the ongoing enterprise transition to private and hybrid clouds. Pressure will mount on datacenters not only to provide increased bandwidth for 3rd Platform applications such as cloud and data analytics but also to deliver the agility and dynamism necessary to accommodate shifting traffic patterns (with more east-west traffic associated with server-to-server flows, as opposed to the traditional north-south traffic associated with client/server computing). Private cloud and legacy applications will also drive daunting bandwidth and connectivity requirements. This Technology Spotlight examines the increasing bandwidth requirements in enterprise datacenters, driven by both new and old application workloads, cloud and noncloud in nature. It also looks at how Cisco is meeting the bandwidth challenge posed by 3rd
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Cisco
Published By: IBM     Published Date: Apr 03, 2017
Businesses today certainly do not suffer from a lack of data. Every day, they capture and consume massive amounts of information that they use to make strategic and tactical decisions. Yet organizations often lack two critical capabilities when it comes to making the right decisions for the business: the ability to make accurate predictions about the future, and to then use those predicted insights in conjunction with organizational goals to identify the best possible actions they should take. The combination of predictive analytics and decision optimization provides organizations with the ability to turn insight into action. Predictive analytics offers insights into likely scenarios by analyzing trends, patterns and relationships in data. Decision optimization prescribes best-action recommendations given an organization’s business goals and business dynamics, taking into account any tradeoffs or consequences associated with those actions.
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predictive analytics, analytics, data analytics, financial marketing, market analytics, data resources, data optimization
    
IBM
Published By: IBM     Published Date: Apr 03, 2017
Predictive analytics is powerful. It can help drive significant improvement to an organization’s bottom line. Look for ways to use it to grow revenue, shrink costs and improve margins. Provide a platform that enables your data scientists to work efficiently using tools and algorithms they prefer. Enhance your analyses with internal and external data, structured and unstructured data. Then make the analytics accessible in order to reap the full benefits of these valuable analyses. Stay ahead of the curve in your market with predictive analytics, and give your organization a competitive advantage and an improved bottom line.
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predictive analytics, analytics, data analytics, financial marketing, market analytics, data resources
    
IBM
Published By: IBM     Published Date: May 01, 2017
If you function like most IT organizations, you've spent the past few years relying on mobile device management (MDM), enterprise mobility management (EMM) and client management tools to get the most out of your enterprise endpoints while limiting the onset of threats you may encounter. In peeling back the onion, you'll find little difference between these conventional tools and strategies in comparison to those that Chief Information Officers (CIOs) and Chief Information Security Officers (CISOs) have employed since the dawn of the modern computing era. Their use has simply become more: Time consuming, with IT trudging through mountains of endpoint data; Inefficient, with limited resources and limitless issues to sort through for opportunities and threats; and Costly, with point solution investments required to address gaps in OS support across available tools. Download this whitepaper to learn how to take advantage of the insights afforded by big data and analytics thereby usher i
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ibm, endpoint management, mobile device management, enterprise mobility, os support, it organizations
    
IBM
Published By: IBM     Published Date: Jun 21, 2017
There are many types of databases and data analysis tools to choose from when building your application. Should you use a relational database? How about a key-value store? Maybe a document database? Is a graph database the right ft? What about polyglot persistence and the need for advanced analytics? If you feel a bit overwhelmed, don’t worry. This guide lays out the various database options and analytic solutions available to meet your app’s unique needs. You’ll see how data can move across databases and development languages, so you can work in your favorite environment without the friction and productivity loss of the past.
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data analysis, key value, document database, analytics
    
IBM
Published By: IBM     Published Date: Jun 21, 2017
NoSQL databases and Apache Spark are a potent combination for rapid integration, transformation and analysis of all kinds of business data. With its data syncing and analytics capabilities, IBM Cloudant offers unique advantages as a NoSQL database for many Spark use cases. IT decision-makers, data scientists and developers need to know how and when to apply these technologies most effectively. IBM can offer a host of resources and tools to help your organization gain value from Cloudant and Spark quickly, and with minimal up-front investment.
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ibm, ibm cloudant, apache spark, nosql, database
    
IBM
Published By: IBM     Published Date: Nov 30, 2017
Analyst firm, Enterprise Strategy Group, examines how companies can leverage cloud-based data lakes and self-service analytics for timely business insights that weren’t possible until now. And learn how IBM Cloud Object Storage, as a persistent storage layer, powers analytics and business intelligence solutions on the IBM Cloud. Complete the form to download the analyst paper.
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analytics, technology, digital transformation, data lake, always-on data lake, ibm, cloud-based analytics
    
IBM
Published By: SAS     Published Date: Jun 05, 2017
Data professionals now have the freedom to create, experiment, test and deploy different methods easily – using whatever skill set they have – all within one cohesive analytics platform. IT leaders gain the ability to centrally manage the entire analytics life cycle for both SAS and other assets with one environment. Organizations get faster results and better ROI from analytics efforts.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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SAS
Published By: SAS     Published Date: Aug 28, 2018
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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SAS
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